Leveraging AI to improve evidence synthesis in conservation

Area of Science:

• Conservation science
• Biodiversity
• Evidence synthesis

Background:

• Systematic evidence syntheses (reviews and maps) are vital for decision-making in applied fields like conservation.
• Current methods for evidence synthesis in conservation are often slow and costly, hindering progress on the biodiversity crisis.

Purpose of the Study:

• To explore the potential benefits and risks of using artificial intelligence (AI), including large language models (LLMs), to improve evidence synthesis in conservation science.

Main Methods:

• Discussion of the integration of AI and LLMs into the evidence synthesis process.
• Analysis of the implications for conservation science.

Main Results:

• AI holds significant promise for accelerating and potentially enhancing the speed and quality of evidence synthesis.
• The judicious application of AI can overcome limitations in resource-intensive traditional methods.

Conclusions:

• AI offers a valuable opportunity to expedite and improve evidence synthesis, particularly for underfunded fields like conservation science.
• Strategic implementation of AI can help address the urgent challenges of the biodiversity crisis more effectively.